DESCRIPTION (Applicant's Description) The long term objective of the project is to develop a new technique for detecting disease clusters called the Entropy Technique. Cluster detection methods are of special importance in environmental health to determine if apparent high disease rates are real as opposed to being statistical fluctuations and to determine the nature and site of clustering. The Entropy Technique is a proposed omnibus method for cluster detection. It works with geographic information systems in situations where controls (non-diseased persons) are available for all cases. It detects the level of clustering by determining the number of ways the cases and controls can be mapped into cells on the space. It provides p values to test the hypothesis of no clustering, The concept of entropy here is similar to that of entropy in thermodynamics. Specifically the aims of this project are to determine the optimal cell size for this technique, to extend the clustering technique to situations in which the ratio of cases to controls is arbitrary, to test it for power against other techniques, and to test it on real data. The general procedure will be to generate synthetic data for a wide range of cluster types. Then the new cluster method will be applied to the synthetic data, and compared for power to other clustering methods used for the same purposes. Finally it will be applied to several sets of real data.